arXiv cs.AI· Arne Hildenbrand, Philipp Diel, Zhuo Cao, Hanno Scharr, Stefan Pischinger, Steffen Marburg·· 4 小时前AI 评分28
声学领域智能体系统的实用性评估
Practical Assessment of Agentic Systems in Acoustics
AI 导读
arXiv 论文评估了大语言模型驱动的智能体在两类常见声学任务中的表现。在车辆内部音频录音的故障检测与定位任务中，智能体达到接近完美的准确率，基准测试表明弱模型会形成任何 harness 设计都无法突破的上限，设计选择只在模型能力足够时才生效。在超材料单元胞设计案例中，智能体可靠地找到了在给定频率范围内实现指定插入损耗的设计。
正文 · 原文
Abstract:In engineering, applications of artificial intelligence have so far been largely limited to surrogate models and optimization tasks. Large language models changed this, as they can write software from a task given in words. Placed inside a harness, a system of components that lets it search for information and execute software, such a model becomes an agent that can take over recurring engineering tasks. This work measures how the model and the elements of the harness shape the performance and behavior of the agent in two common acoustic applications. In the first, the agent detects and locates faults in audio recordings from a vehicle interior with near-perfect accuracy. A benchmark on this task shows that a weak model sets a limit that no harness design can overcome, and that design choices take effect only once the model is capable enough to use them. In the second, a case study on the design of a metamaterial unit cell, the agent reliably finds designs that achieve a specified insertion loss over a given frequency range. The results aim to help the reader understand what each element of an agent contributes and how to design an agent for acoustic tasks.
| Subjects: | Sound (cs.SD); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.39685 [cs.SD] |
| (or arXiv:2610.39685v1 [cs.SD] for this version) | |
| https://doi.org/10.48550/arXiv.2610.39685 arXiv-issued DOI via DataCite |
Submission history
From: Arne Erik Hildenbrand [view email]
[v1]
Thu, 29 Oct 2026 09:29:07 UTC (2,591 KB)
来源：arXiv cs.AI · arxiv.org